Managing the container yard operations can be challenging as a result of various uncertainties associated with storing and retrieving containers from the yard. These associated uncertainties occur because the arrival ...
详细信息
Managing the container yard operations can be challenging as a result of various uncertainties associated with storing and retrieving containers from the yard. These associated uncertainties occur because the arrival of a truck to pick up the container is random, so the departure time of the container is unknown. The problem investigated in this paper emerges when newly arrived containers of different sizes, types and weights require storage operation in the same yard where other containers have already been stored. This situation becomes more challenging when the time of departure of existing container is not known. This study develops a new Fuzzy Knowledge-Based optimisation system named 'FKB_GA' for optimal storage and retrieval of containers in a yard that contains long stay pre-existing containers. The containers' duration of stay factor is considered along with two other factors such as the similarity (containers with same customer) and the quantity of containers per stack. A new multi-layered geneticalgorithm module is proposed which identifies the optimal fuzzy rules required for each set of fired rules to achieve a minimum number of container re-handlings when selecting a stack. An industrial case study is used to demonstrate the applicability and practicability of the developed system.
The railway handling area of the railway container terminal is the main place for container sea-rail transportation. To enhance the efficiency of the handling systems and improve the coordination of different types of...
详细信息
The railway handling area of the railway container terminal is the main place for container sea-rail transportation. To enhance the efficiency of the handling systems and improve the coordination of different types of equipment in railway handling area, an integrated scheduling model is developed, which integrates the scheduling of rail-mounted gantry cranes (GCs), inner trucks (ITs), and yard cranes (YCs). This model considers not only train loading and unloading simultaneously, but GC interference and safety margin, GC and YC travel time as well as buffer area. A multi-layer genetic algorithm (MLGA) is proposed to solve the problem. Eventually, comparison experiments are conducted, including the experiments on handling process between non-optimization and optimization, MLGA with and without the balanced operator and the performance of different numbers of ITs. These experiments validate that the model and algorithm is feasible and efficient in both equipment scheduling and allocation problem.
Cross-matching puzzles are logic based games being played with numbers, letters or symbols that present combinational problems. A cross-matching puzzle consists of three tables: solution table, detection table, and co...
详细信息
Cross-matching puzzles are logic based games being played with numbers, letters or symbols that present combinational problems. A cross-matching puzzle consists of three tables: solution table, detection table, and control table. The puzzle can be solved by superposing the detection and control tables. For the solution of the cross-matching puzzle, a depth first search method can be used, but by expanding the size of the puzzle, computing time can be increased. Hence, the geneticalgorithm, which is one of the most common optimization algorithms, was used to solve cross-matching puzzles. The multi-layer genetic algorithm was improved for the solution of cross-matching puzzles, but the results of the multi-layer genetic algorithm were not good enough because of the expanding size of the puzzle. Therefore, in this study, the geneticalgorithm was improved in an intelligent way due to the structure of the puzzle. The obtained results showed that an intelligent geneticalgorithm can be used to solve cross-matching puzzles.
暂无评论